Overview

Dataset statistics

Number of variables13
Number of observations5570
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory989.4 KiB
Average record size in memory181.9 B

Variable types

Numeric12
Categorical1

Alerts

Município has a high cardinality: 5570 distinct values High cardinality
CV_HEPatite_B is highly correlated with CV_HIB and 8 other fieldsHigh correlation
CV_HIB is highly correlated with CV_HEPatite_B and 8 other fieldsHigh correlation
CV_DPT is highly correlated with CV_HEPatite_B and 8 other fieldsHigh correlation
CV_POLIO is highly correlated with CV_HEPatite_B and 8 other fieldsHigh correlation
CV_Pneumo is highly correlated with CV_HEPatite_B and 8 other fieldsHigh correlation
CV_MncC is highly correlated with CV_HEPatite_B and 8 other fieldsHigh correlation
CV_SCR1 is highly correlated with CV_HEPatite_B and 8 other fieldsHigh correlation
CV_SCR2 is highly correlated with CV_HEPatite_B and 8 other fieldsHigh correlation
CV_VARICELA is highly correlated with CV_HEPatite_B and 8 other fieldsHigh correlation
CV_HEPatite_A is highly correlated with CV_HEPatite_B and 8 other fieldsHigh correlation
Município is uniformly distributed Uniform
COD has unique values Unique
Município has unique values Unique
CV_BCG has 326 (5.9%) zeros Zeros

Reproduction

Analysis started2022-11-09 00:43:30.167382
Analysis finished2022-11-09 00:43:50.394813
Duration20.23 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

COD
Real number (ℝ≥0)

UNIQUE

Distinct5570
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325358.6278
Minimum110001
Maximum530010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:43:50.506811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum110001
5-th percentile150777.25
Q1251212.5
median314627.5
Q3411918.75
95-th percentile510729.55
Maximum530010
Range420009
Interquartile range (IQR)160706.25

Descriptive statistics

Standard deviation98491.03388
Coefficient of variation (CV)0.3027152977
Kurtosis-0.5258091553
Mean325358.6278
Median Absolute Deviation (MAD)74152.5
Skewness0.1213411839
Sum1812247557
Variance9700483754
MonotonicityStrictly increasing
2022-11-08T21:43:50.640811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100011
 
< 0.1%
3539701
 
< 0.1%
3540401
 
< 0.1%
3540301
 
< 0.1%
3540251
 
< 0.1%
3540201
 
< 0.1%
3540101
 
< 0.1%
3540001
 
< 0.1%
3539901
 
< 0.1%
3539801
 
< 0.1%
Other values (5560)5560
99.8%
ValueCountFrequency (%)
1100011
< 0.1%
1100021
< 0.1%
1100031
< 0.1%
1100041
< 0.1%
1100051
< 0.1%
1100061
< 0.1%
1100071
< 0.1%
1100081
< 0.1%
1100091
< 0.1%
1100101
< 0.1%
ValueCountFrequency (%)
5300101
< 0.1%
5222301
< 0.1%
5222201
< 0.1%
5222051
< 0.1%
5222001
< 0.1%
5221901
< 0.1%
5221851
< 0.1%
5221801
< 0.1%
5221701
< 0.1%
5221601
< 0.1%

Município
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct5570
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size467.3 KiB
110001 Alta Floresta D'Oeste
 
1
353970 Platina
 
1
354040 Populina
 
1
354030 Pontes Gestal
 
1
354025 Pontalinda
 
1
Other values (5565)
5565 

Length

Max length39
Median length34
Mean length18.61059246
Min length10

Characters and Unicode

Total characters103661
Distinct characters80
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5570 ?
Unique (%)100.0%

Sample

1st row110001 Alta Floresta D'Oeste
2nd row110002 Ariquemes
3rd row110003 Cabixi
4th row110004 Cacoal
5th row110005 Cerejeiras

Common Values

ValueCountFrequency (%)
110001 Alta Floresta D'Oeste1
 
< 0.1%
353970 Platina1
 
< 0.1%
354040 Populina1
 
< 0.1%
354030 Pontes Gestal1
 
< 0.1%
354025 Pontalinda1
 
< 0.1%
354020 Pontal1
 
< 0.1%
354010 Pongaí1
 
< 0.1%
354000 Pompéia1
 
< 0.1%
353990 Poloni1
 
< 0.1%
353980 Poá1
 
< 0.1%
Other values (5560)5560
99.8%

Length

2022-11-08T21:43:50.823814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
do756
 
4.8%
são364
 
2.3%
de302
 
1.9%
santa161
 
1.0%
da143
 
0.9%
nova135
 
0.9%
sul115
 
0.7%
rio94
 
0.6%
dos73
 
0.5%
josé70
 
0.4%
Other values (9533)13640
86.0%

Most occurring characters

ValueCountFrequency (%)
10283
 
9.9%
a8791
 
8.5%
08160
 
7.9%
o5961
 
5.8%
14774
 
4.6%
24591
 
4.4%
r4532
 
4.4%
i4388
 
4.2%
34106
 
4.0%
e3764
 
3.6%
Other values (70)44311
42.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter50872
49.1%
Decimal Number33420
32.2%
Space Separator10283
 
9.9%
Uppercase Letter9010
 
8.7%
Other Punctuation47
 
< 0.1%
Dash Punctuation29
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a8791
17.3%
o5961
11.7%
r4532
8.9%
i4388
8.6%
e3764
 
7.4%
n3196
 
6.3%
d2553
 
5.0%
s2423
 
4.8%
t2293
 
4.5%
u2155
 
4.2%
Other values (27)10816
21.3%
Uppercase Letter
ValueCountFrequency (%)
S1137
12.6%
C970
10.8%
P911
 
10.1%
M721
 
8.0%
A698
 
7.7%
B602
 
6.7%
I475
 
5.3%
J405
 
4.5%
G391
 
4.3%
R367
 
4.1%
Other values (20)2333
25.9%
Decimal Number
ValueCountFrequency (%)
08160
24.4%
14774
14.3%
24591
13.7%
34106
12.3%
53654
10.9%
42781
 
8.3%
71470
 
4.4%
61422
 
4.3%
91382
 
4.1%
81080
 
3.2%
Space Separator
ValueCountFrequency (%)
10283
100.0%
Other Punctuation
ValueCountFrequency (%)
'47
100.0%
Dash Punctuation
ValueCountFrequency (%)
-29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin59882
57.8%
Common43779
42.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a8791
14.7%
o5961
 
10.0%
r4532
 
7.6%
i4388
 
7.3%
e3764
 
6.3%
n3196
 
5.3%
d2553
 
4.3%
s2423
 
4.0%
t2293
 
3.8%
u2155
 
3.6%
Other values (57)19826
33.1%
Common
ValueCountFrequency (%)
10283
23.5%
08160
18.6%
14774
10.9%
24591
10.5%
34106
 
9.4%
53654
 
8.3%
42781
 
6.4%
71470
 
3.4%
61422
 
3.2%
91382
 
3.2%
Other values (3)1156
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII100822
97.3%
None2839
 
2.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10283
 
10.2%
a8791
 
8.7%
08160
 
8.1%
o5961
 
5.9%
14774
 
4.7%
24591
 
4.6%
r4532
 
4.5%
i4388
 
4.4%
34106
 
4.1%
e3764
 
3.7%
Other values (54)41472
41.1%
None
ValueCountFrequency (%)
ã794
28.0%
á393
13.8%
í336
11.8%
é317
 
11.2%
ç268
 
9.4%
ó243
 
8.6%
â161
 
5.7%
ú101
 
3.6%
ô71
 
2.5%
ê70
 
2.5%
Other values (6)85
 
3.0%

CV_BCG
Real number (ℝ≥0)

ZEROS

Distinct3664
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.63224417
Minimum0
Maximum701.31
Zeros326
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:43:50.977812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122.04
median59.215
Q390.91
95-th percentile122.0005
Maximum701.31
Range701.31
Interquartile range (IQR)68.87

Descriptive statistics

Standard deviation44.36443295
Coefficient of variation (CV)0.7439671871
Kurtosis16.05381678
Mean59.63224417
Median Absolute Deviation (MAD)34.215
Skewness1.753687951
Sum332151.6
Variance1968.202911
MonotonicityNot monotonic
2022-11-08T21:43:51.131811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0326
 
5.9%
10047
 
0.8%
5020
 
0.4%
33.3316
 
0.3%
2514
 
0.3%
66.6712
 
0.2%
14.2911
 
0.2%
87.511
 
0.2%
12.511
 
0.2%
6010
 
0.2%
Other values (3654)5092
91.4%
ValueCountFrequency (%)
0326
5.9%
0.21
 
< 0.1%
0.211
 
< 0.1%
0.241
 
< 0.1%
0.281
 
< 0.1%
0.291
 
< 0.1%
0.341
 
< 0.1%
0.41
 
< 0.1%
0.421
 
< 0.1%
0.431
 
< 0.1%
ValueCountFrequency (%)
701.311
< 0.1%
630.221
< 0.1%
469.311
< 0.1%
437.831
< 0.1%
409.21
< 0.1%
334.311
< 0.1%
326.921
< 0.1%
316.461
< 0.1%
309.761
< 0.1%
284.231
< 0.1%

CV_HEPatite_B
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3570
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.80218851
Minimum0
Maximum510
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:43:51.410810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.3065
Q174.58
median94.58
Q3112
95-th percentile143.5405
Maximum510
Range510
Interquartile range (IQR)37.42

Descriptive statistics

Standard deviation32.06833416
Coefficient of variation (CV)0.3418719186
Kurtosis6.924718488
Mean93.80218851
Median Absolute Deviation (MAD)18.46
Skewness0.7275547686
Sum522478.19
Variance1028.378056
MonotonicityNot monotonic
2022-11-08T21:43:51.546811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10085
 
1.5%
15018
 
0.3%
12515
 
0.3%
116.6715
 
0.3%
12014
 
0.3%
112.514
 
0.3%
66.6713
 
0.2%
109.0913
 
0.2%
133.3313
 
0.2%
128.5712
 
0.2%
Other values (3560)5358
96.2%
ValueCountFrequency (%)
05
0.1%
0.31
 
< 0.1%
0.751
 
< 0.1%
3.321
 
< 0.1%
4.761
 
< 0.1%
4.781
 
< 0.1%
4.841
 
< 0.1%
5.421
 
< 0.1%
6.131
 
< 0.1%
7.091
 
< 0.1%
ValueCountFrequency (%)
5101
 
< 0.1%
338.461
 
< 0.1%
3001
 
< 0.1%
266.41
 
< 0.1%
257.142
< 0.1%
253.851
 
< 0.1%
2501
 
< 0.1%
244.831
 
< 0.1%
2253
0.1%
221.741
 
< 0.1%

CV_HIB
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3592
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.78474686
Minimum0
Maximum510
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:43:51.676812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.239
Q174.3875
median94.535
Q3112.025
95-th percentile143.6175
Maximum510
Range510
Interquartile range (IQR)37.6375

Descriptive statistics

Standard deviation32.12515672
Coefficient of variation (CV)0.3425413812
Kurtosis6.830354754
Mean93.78474686
Median Absolute Deviation (MAD)18.545
Skewness0.7225574621
Sum522381.04
Variance1032.025694
MonotonicityNot monotonic
2022-11-08T21:43:51.817810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10084
 
1.5%
15018
 
0.3%
12017
 
0.3%
66.6715
 
0.3%
12514
 
0.3%
112.514
 
0.3%
116.6713
 
0.2%
133.3312
 
0.2%
83.3312
 
0.2%
88.8912
 
0.2%
Other values (3582)5359
96.2%
ValueCountFrequency (%)
05
0.1%
0.31
 
< 0.1%
0.751
 
< 0.1%
2.971
 
< 0.1%
4.761
 
< 0.1%
4.781
 
< 0.1%
4.841
 
< 0.1%
5.421
 
< 0.1%
6.131
 
< 0.1%
7.091
 
< 0.1%
ValueCountFrequency (%)
5101
< 0.1%
330.771
< 0.1%
3001
< 0.1%
266.731
< 0.1%
257.142
< 0.1%
253.851
< 0.1%
2501
< 0.1%
244.831
< 0.1%
241.671
< 0.1%
2252
< 0.1%

CV_DPT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3639
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.9977684
Minimum0
Maximum510
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:43:51.945811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.323
Q174.8425
median94.655
Q3112.155
95-th percentile143.6455
Maximum510
Range510
Interquartile range (IQR)37.3125

Descriptive statistics

Standard deviation32.10889569
Coefficient of variation (CV)0.3415921063
Kurtosis6.834642154
Mean93.9977684
Median Absolute Deviation (MAD)18.52
Skewness0.7223296629
Sum523567.57
Variance1030.981183
MonotonicityNot monotonic
2022-11-08T21:43:52.118811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10088
 
1.6%
12018
 
0.3%
15017
 
0.3%
128.5715
 
0.3%
12515
 
0.3%
66.6715
 
0.3%
116.6715
 
0.3%
133.3314
 
0.3%
112.514
 
0.3%
114.2912
 
0.2%
Other values (3629)5347
96.0%
ValueCountFrequency (%)
05
0.1%
0.31
 
< 0.1%
0.751
 
< 0.1%
3.321
 
< 0.1%
4.761
 
< 0.1%
4.781
 
< 0.1%
4.841
 
< 0.1%
5.421
 
< 0.1%
6.131
 
< 0.1%
7.091
 
< 0.1%
ValueCountFrequency (%)
5101
< 0.1%
330.771
< 0.1%
3001
< 0.1%
267.731
< 0.1%
257.142
< 0.1%
253.851
< 0.1%
2501
< 0.1%
244.831
< 0.1%
241.671
< 0.1%
2252
< 0.1%

CV_POLIO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3473
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.73294434
Minimum0
Maximum500
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:43:52.273813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.4235
Q175.8425
median92.34
Q3106.935
95-th percentile134.5345
Maximum500
Range500
Interquartile range (IQR)31.0925

Descriptive statistics

Standard deviation28.39977273
Coefficient of variation (CV)0.3095918586
Kurtosis12.71882021
Mean91.73294434
Median Absolute Deviation (MAD)15.515
Skewness1.084955645
Sum510952.5
Variance806.5470913
MonotonicityNot monotonic
2022-11-08T21:43:52.450812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100113
 
2.0%
88.8917
 
0.3%
66.6716
 
0.3%
116.6715
 
0.3%
9015
 
0.3%
133.3314
 
0.3%
12014
 
0.3%
12513
 
0.2%
128.5713
 
0.2%
111.1112
 
0.2%
Other values (3463)5328
95.7%
ValueCountFrequency (%)
04
0.1%
0.591
 
< 0.1%
1.131
 
< 0.1%
4.761
 
< 0.1%
5.081
 
< 0.1%
5.221
 
< 0.1%
5.361
 
< 0.1%
5.931
 
< 0.1%
7.141
 
< 0.1%
7.191
 
< 0.1%
ValueCountFrequency (%)
5001
< 0.1%
423.081
< 0.1%
291.431
< 0.1%
262.161
< 0.1%
257.141
< 0.1%
2501
< 0.1%
246.431
< 0.1%
243.751
< 0.1%
2321
< 0.1%
231.031
< 0.1%

CV_Pneumo
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3400
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.53718851
Minimum0
Maximum440
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:43:52.612813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50.197
Q181.25
median96.295
Q3109.91
95-th percentile136.2835
Maximum440
Range440
Interquartile range (IQR)28.66

Descriptive statistics

Standard deviation27.26761444
Coefficient of variation (CV)0.2854136161
Kurtosis10.67271196
Mean95.53718851
Median Absolute Deviation (MAD)14.3
Skewness0.8332290648
Sum532142.14
Variance743.5227973
MonotonicityNot monotonic
2022-11-08T21:43:52.767815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100112
 
2.0%
111.1122
 
0.4%
12520
 
0.4%
12018
 
0.3%
133.3316
 
0.3%
112.514
 
0.3%
15014
 
0.3%
8012
 
0.2%
7512
 
0.2%
87.512
 
0.2%
Other values (3390)5318
95.5%
ValueCountFrequency (%)
05
0.1%
0.751
 
< 0.1%
4.751
 
< 0.1%
5.541
 
< 0.1%
5.651
 
< 0.1%
6.211
 
< 0.1%
6.441
 
< 0.1%
6.961
 
< 0.1%
7.061
 
< 0.1%
8.481
 
< 0.1%
ValueCountFrequency (%)
4401
< 0.1%
430.771
< 0.1%
3001
< 0.1%
280.631
< 0.1%
237.51
< 0.1%
2351
< 0.1%
228.572
< 0.1%
212.821
< 0.1%
2121
< 0.1%
206.251
< 0.1%

CV_MncC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3417
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.35078456
Minimum0
Maximum430
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:43:52.921810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47.8775
Q178.59
median94.12
Q3107.55
95-th percentile134.7305
Maximum430
Range430
Interquartile range (IQR)28.96

Descriptive statistics

Standard deviation27.499909
Coefficient of variation (CV)0.294586801
Kurtosis10.99428273
Mean93.35078456
Median Absolute Deviation (MAD)14.495
Skewness0.9682587377
Sum519963.87
Variance756.2449949
MonotonicityNot monotonic
2022-11-08T21:43:53.086813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100111
 
2.0%
88.8918
 
0.3%
133.3318
 
0.3%
108.3316
 
0.3%
83.3316
 
0.3%
112.514
 
0.3%
66.6714
 
0.3%
12014
 
0.3%
11013
 
0.2%
116.6712
 
0.2%
Other values (3407)5324
95.6%
ValueCountFrequency (%)
04
0.1%
0.31
 
< 0.1%
0.751
 
< 0.1%
3.261
 
< 0.1%
3.571
 
< 0.1%
5.291
 
< 0.1%
5.761
 
< 0.1%
6.061
 
< 0.1%
6.631
 
< 0.1%
7.641
 
< 0.1%
ValueCountFrequency (%)
4301
< 0.1%
423.081
< 0.1%
3501
< 0.1%
3201
< 0.1%
263.591
< 0.1%
228.571
< 0.1%
2251
< 0.1%
215.381
< 0.1%
2121
< 0.1%
210.711
< 0.1%

CV_SCR1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3538
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.74959425
Minimum0
Maximum341.67
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:43:53.231812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45.2455
Q177.5775
median95.035
Q3109.52
95-th percentile137.5
Maximum341.67
Range341.67
Interquartile range (IQR)31.9425

Descriptive statistics

Standard deviation29.0318525
Coefficient of variation (CV)0.3096744336
Kurtosis4.270358906
Mean93.74959425
Median Absolute Deviation (MAD)15.885
Skewness0.546428078
Sum522185.24
Variance842.8484596
MonotonicityNot monotonic
2022-11-08T21:43:53.393810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100106
 
1.9%
114.2919
 
0.3%
15019
 
0.3%
12517
 
0.3%
116.6717
 
0.3%
91.6716
 
0.3%
66.6716
 
0.3%
12015
 
0.3%
133.3314
 
0.3%
7513
 
0.2%
Other values (3528)5318
95.5%
ValueCountFrequency (%)
04
0.1%
1.371
 
< 0.1%
2.671
 
< 0.1%
3.391
 
< 0.1%
4.271
 
< 0.1%
4.391
 
< 0.1%
4.561
 
< 0.1%
6.891
 
< 0.1%
7.631
 
< 0.1%
8.251
 
< 0.1%
ValueCountFrequency (%)
341.671
< 0.1%
316.671
< 0.1%
284.211
< 0.1%
277.271
< 0.1%
2751
< 0.1%
273.081
< 0.1%
269.231
< 0.1%
251.981
< 0.1%
2502
< 0.1%
242.421
< 0.1%

CV_SCR2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3732
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.77722442
Minimum0
Maximum525
Zeros38
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:43:53.549812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.51
Q153.5
median76.13
Q396.245
95-th percentile128.4355
Maximum525
Range525
Interquartile range (IQR)42.745

Descriptive statistics

Standard deviation33.79979589
Coefficient of variation (CV)0.4460416194
Kurtosis6.924374083
Mean75.77722442
Median Absolute Deviation (MAD)21.26
Skewness0.792160371
Sum422079.14
Variance1142.426202
MonotonicityNot monotonic
2022-11-08T21:43:53.873813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10073
 
1.3%
038
 
0.7%
5034
 
0.6%
7516
 
0.3%
114.2914
 
0.3%
8014
 
0.3%
12014
 
0.3%
66.6714
 
0.3%
85.7113
 
0.2%
6013
 
0.2%
Other values (3722)5327
95.6%
ValueCountFrequency (%)
038
0.7%
0.441
 
< 0.1%
0.681
 
< 0.1%
1.391
 
< 0.1%
1.571
 
< 0.1%
1.691
 
< 0.1%
1.961
 
< 0.1%
2.131
 
< 0.1%
2.141
 
< 0.1%
2.151
 
< 0.1%
ValueCountFrequency (%)
5251
< 0.1%
346.151
< 0.1%
272.731
< 0.1%
2501
< 0.1%
2401
< 0.1%
229.411
< 0.1%
2251
< 0.1%
223.331
< 0.1%
216.671
< 0.1%
214.291
< 0.1%

CV_VARICELA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3629
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.3534219
Minimum0
Maximum325
Zeros9
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:43:54.022813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36.8845
Q169.175
median88.45
Q3105.88
95-th percentile138.89
Maximum325
Range325
Interquartile range (IQR)36.705

Descriptive statistics

Standard deviation31.5377524
Coefficient of variation (CV)0.3569499825
Kurtosis2.784775776
Mean88.3534219
Median Absolute Deviation (MAD)18.305
Skewness0.5944701833
Sum492128.56
Variance994.6298266
MonotonicityNot monotonic
2022-11-08T21:43:54.184813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10074
 
1.3%
8020
 
0.4%
85.7119
 
0.3%
11015
 
0.3%
83.3315
 
0.3%
12014
 
0.3%
66.6713
 
0.2%
116.6713
 
0.2%
12512
 
0.2%
87.512
 
0.2%
Other values (3619)5363
96.3%
ValueCountFrequency (%)
09
0.2%
0.331
 
< 0.1%
2.831
 
< 0.1%
4.671
 
< 0.1%
4.761
 
< 0.1%
4.881
 
< 0.1%
5.531
 
< 0.1%
5.561
 
< 0.1%
5.951
 
< 0.1%
6.061
 
< 0.1%
ValueCountFrequency (%)
3251
< 0.1%
296.151
< 0.1%
282.51
< 0.1%
270.591
< 0.1%
266.671
< 0.1%
263.641
< 0.1%
254.551
< 0.1%
2501
< 0.1%
241.181
< 0.1%
234.161
< 0.1%

CV_HEPatite_A
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3582
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.94414004
Minimum0
Maximum375
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:43:54.368812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.108
Q172.625
median91.215
Q3106.85
95-th percentile136.36
Maximum375
Range375
Interquartile range (IQR)34.225

Descriptive statistics

Standard deviation30.47236068
Coefficient of variation (CV)0.3387920621
Kurtosis5.20248621
Mean89.94414004
Median Absolute Deviation (MAD)17.105
Skewness0.6250970379
Sum500988.86
Variance928.5647653
MonotonicityNot monotonic
2022-11-08T21:43:54.541810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100104
 
1.9%
12517
 
0.3%
12017
 
0.3%
8017
 
0.3%
114.2915
 
0.3%
118.1814
 
0.3%
107.6913
 
0.2%
87.513
 
0.2%
5012
 
0.2%
15012
 
0.2%
Other values (3572)5336
95.8%
ValueCountFrequency (%)
08
0.1%
0.331
 
< 0.1%
0.671
 
< 0.1%
0.681
 
< 0.1%
1.221
 
< 0.1%
2.671
 
< 0.1%
3.391
 
< 0.1%
4.561
 
< 0.1%
4.761
 
< 0.1%
5.381
 
< 0.1%
ValueCountFrequency (%)
3751
< 0.1%
338.461
< 0.1%
336.361
< 0.1%
3251
< 0.1%
308.331
< 0.1%
286.671
< 0.1%
254.171
< 0.1%
241.671
< 0.1%
241.181
< 0.1%
226.671
< 0.1%

Interactions

2022-11-08T21:43:48.572814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:32.896384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:34.224385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:35.575443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:37.014463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:38.332877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:39.761878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:41.094874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:42.557877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:43.963909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:45.496914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:46.977914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:48.702815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:33.026382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:34.342384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:35.792422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:37.122878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:38.440878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:39.869878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:41.202875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:42.670875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:44.076910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:45.605909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:47.096910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:48.821812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:33.139384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:34.454384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:35.905424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:37.233877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:38.552878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:39.980878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:41.316874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:42.791876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:44.194910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:45.773912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:47.215914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:48.940810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:33.246382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:34.567131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:36.014424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:37.342876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:38.779877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:40.090873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:41.425875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:42.912876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:44.309908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:45.895909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:47.346913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:49.055814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:33.354382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:34.679131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:36.122286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:37.451878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:38.887878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:40.201876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:41.537874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:43.030873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:44.424909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:46.009912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:47.473814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:49.171815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:33.462385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:34.792204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:36.230439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:37.562878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:38.997873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:40.309873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:41.644872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:43.147875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:44.540910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:46.124911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:47.601815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:49.300812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:33.574384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:34.903204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:36.347453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:37.671878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:39.105873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:40.421876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:41.880873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:43.266875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:44.659911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:46.255911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:47.722811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:49.442813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:33.680383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:35.014204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:36.462454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:37.778874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:39.212878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:40.530876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:41.987873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:43.379911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:44.776910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:46.368914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:47.842814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:49.586814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:33.785383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:35.123205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:36.570449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:37.886878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:39.319877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:40.638872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:42.095875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:43.490910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:44.889911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:46.483913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:47.962816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:49.732816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:33.899384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:35.237205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:36.683430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:38.000878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:39.432876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:40.753875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:42.207873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:43.608912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:45.138911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:46.615914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:48.086814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:49.866815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:34.005382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:35.347207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:36.793447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:38.108877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:39.541877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:40.865875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:42.317875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:43.730908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:45.251914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:46.736914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:48.202811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:49.983810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:34.114384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:35.460202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:36.903463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:38.218878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:39.650879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:40.979875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:42.434876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:43.844908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:45.364914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:46.855910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:43:48.442813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-08T21:43:54.687814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-08T21:43:54.862811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-08T21:43:55.043813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-08T21:43:55.220814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-08T21:43:55.376812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-08T21:43:50.167810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-08T21:43:50.340812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

CODMunicípioCV_BCGCV_HEPatite_BCV_HIBCV_DPTCV_POLIOCV_PneumoCV_MncCCV_SCR1CV_SCR2CV_VARICELACV_HEPatite_A
0110001110001 Alta Floresta D'Oeste86.49112.01112.01112.31104.80108.71105.71100.8279.4091.2195.60
1110002110002 Ariquemes102.06104.45104.58104.5897.28100.2095.3692.1173.0082.8886.09
2110003110003 Cabixi0.00126.09126.09127.54120.29117.39107.25105.1387.1892.3187.18
3110004110004 Cacoal104.83103.40103.47103.4791.8596.4593.21105.4160.0687.5691.37
4110005110005 Cerejeiras69.52109.29109.29109.67108.55111.90106.69112.88100.43106.01109.01
5110006110006 Colorado do Oeste16.75124.63125.62125.12112.81118.72115.27110.3398.5994.84104.69
6110007110007 Corumbiara21.50132.71132.71132.71135.51114.95119.63114.1777.9597.64101.57
7110008110008 Costa Marques83.68115.26115.26115.26107.37114.21107.8990.7596.0494.2799.56
8110009110009 Espigão D'Oeste88.9696.1896.1896.1884.5099.5894.4884.1658.3789.5989.37
9110010110010 Guajará-Mirim50.5853.5053.5053.6150.9357.1156.1854.5939.2049.2949.42

Last rows

CODMunicípioCV_BCGCV_HEPatite_BCV_HIBCV_DPTCV_POLIOCV_PneumoCV_MncCCV_SCR1CV_SCR2CV_VARICELACV_HEPatite_A
5560522160522160 Uruaçu96.2367.7467.7467.7480.1985.6678.3072.5956.7266.9074.83
5561522170522170 Uruana32.5942.9642.9642.9651.1165.1959.2660.9361.5960.9362.91
5562522180522180 Urutaí70.83129.17129.17129.17125.00133.3391.67275.00216.67216.67308.33
5563522185522185 Valparaíso de Goiás44.1568.4468.4468.7268.8176.3173.1262.4860.0258.6167.85
5564522190522190 Varjão113.79244.83244.83244.83231.03155.17186.21223.33196.67220.00203.33
5565522200522200 Vianópolis105.81107.56106.98106.98122.67129.07124.42117.9585.1385.64113.33
5566522205522205 Vicentinópolis51.0891.3791.3791.3777.7074.1074.1066.9769.7269.7281.65
5567522220522220 Vila Boa45.10131.37131.37131.37123.53101.96115.69142.8689.80112.24124.49
5568522230522230 Vila Propício41.5190.5790.5790.5788.68122.64118.8780.6073.1373.1383.58
5569530010530010 Brasília105.15100.80101.27101.3692.3096.9894.0084.4072.5682.0485.27